According to characteristics of the centrifugal fan fault signals

such as non-stationary

non-linear and sample scarcity during actual application process

a new method of ensemble empirical mode decomposition sample entropy(SE)and support vector machine(SVM)fan fault diagnosis was proposed.In this method

the axial displacement signal is decomposed into a series of stable intrinsic mode functions(IMF)by the EEMD algorithm firstly.Then

the phony mode components of the IMF are filtered and removed according to the correlation coefficient rule

and the entropy of the filtered IMF is calculate respectively and then be considered as the eigenvectors.Finally

the eigenvectors are put into the LIBSVM and BP neural network to carry out mode identification.At last

the feasibility

effectiveness and superiority of the above method are verified by experiments.